Panel Discussion: Business Side of Data Science
summary
Panelists cite concrete claims and measures: 92 percent say culture not tech blocks adoption, surveys report one in four AI initiatives reach production but several argue the real rate is lower. Names and tools drop in the discussion—Snowflake Databricks and Zing double g dot AI are mentioned—alongside a concrete delivery pattern: start with a CSV export of 100 prospects as a POC, measure lift (example cited: ~5%), then decide whether to engineer for production.
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chapters (9)
- 0:00Panel discussion intro
- 1:41Panelists and backgrounds
- 4:33Data science to business value
- 7:05Culture blocks data adoption
- 16:35Managing C-level expectations
- 23:39Legacy companies and data asset
- 30:35Avoiding POC shelfware
- 37:20Soft skills for data scientists
- 45:10Closing remarks
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